Surveillance systems often include stationary camera around the monitored scene for surveillance. When analyzing the images, technologies, such as, background subtraction or frame difference, are used to remove the background and detect the moving foreground. When the camera is installed on a moving vehicle, the processing and analysis of the captured images will encounter the problems of continuously changing background, small moving object relative to wide region, slow relative moving speed, and so on. This also complicates the aerial image of moving object detection in comparison with stationary camera.
Related researches are developed to use automatic intelligent visual surveillance technology in the future city and battlefield, where the moving object detection is based on the affine warping technology to make successive input images to achieve mutual registration. After image stabilization, the technology computes the normal flow of two successive stabilized images to detect the moving object. Also, the 4-connectivity connected component labeling technology is used to label moving object, and the attributes, such as, location of mass center, axial direction and length, of labeled object in each image are taken into account to compute the similarity and correlation of the objects in neighboring images for tracking moving object.
Moving object detection methods may be divided into optical flow method and frame difference method. The optical flow method is to differentiate moving object from the background by computing displacement of each pixel of image along the time and comparing the dominant motion and local motion. When the object or the background is larger and more uniform, the optical flow of the image frame is difficult to compute. The frame difference method is to compute frame differences to differentiate the moving object from the background after aligning neighboring images. When the object has uniform color and larger in size, it is easy to obtain fragmented result.
There are many prior arts for moving object detection. For example, Taiwan Patent Publication No. 200823800 discloses a method for dividing video object. As shown in the exemplary flowchart of FIG. 1, the video object division method is to compute the corresponding pixel difference of pixels of a current image Fn and the pixels of a previous image Fn−1, then uses the difference and the pixel to execute multi-level background registration to extract a background Bn, and then uses background Bn to remove the background region of the image. Finally, by using the background Bn′ with removed brightness average to remove the background region of the image Fn′ with removed brightness average, the method may obtain the foreground image.
As shown in the exemplary flowchart of FIG. 2, China Patent Publication CN101087413 discloses a method for dividing the moving object in video sequence. The method is to perform frame difference between the current image and the previous image, and computes statistic analysis. Combined with edge detection result, the moving object profile can be obtained. Then, the horizontal and vertical filling method is used to detect moving object. This division method is applicable to the detection of a single moving object.
Moving object tracking methods may be divided into three types. The first type is to track by using KLT tracker to associate the objects in the neighboring images. The second type is to compute the appearance and motion characteristics of the object, or to determine the correlation of the moving objects in neighboring images through setting a threshold, or based on multiple hypo thesis tracker (MHT) or joint probability data correlation (JPDA), and takes the characteristics of a plurality of moving objects in the image into account to compute the optimal match probability. The third type is to use filter technology, such as particle filter, to execute moving object tracking.
The conventional moving object tracking technology, such as U.S. Patent Publication No. 2007/0250260, discloses a method and system for autonomous tracking of a mobile target by an unmanned aerial vehicle. As shown in the exemplary embodiment of FIG. 3, aerial tracking system 300 uses an aircraft vehicle 312 carrying sensor 314 to autonomously track ground moving vehicles 310. Aerial tracking system 300 models the motion mode of both ground moving vehicle 310 and aircraft, aided by prediction and update capability of Kalman filter to track ground moving vehicle 310, where field of view (FOV) projected by sensor 314 has a ground FOV circle 320, and the angle of view of sensor 314 is equal to conic angle θ of the cone vertically beneath aircraft vehicle 312.
U.S. Pat. No. 7,136,506 disclosed a video correlation tracking system, by comparing the correlation of the image patch in successive images as a base for moving object tracking, where the user sets the location and the range of the object to be tracked in the initial image. U.S. Patent Publication No. US2009/0022366 disclosed an image analysis system. The image comes from a non-static camera, and the system uses a camera with a fixed searching path to monitor a wide area, uses image concatenation to construct the full image and uses background subtraction technique to detect moving object.
U.S. Patent Publication No. 2007/0268364 disclosed a moving object detection system, targeting successive images to perform image alignment and then computing the motion similarity of the pixels in the image. If the area with consistent motion similarity matches the size of the object, the area is determined to be moving object. The moving object obtained by this system may not have a complete outline.